My first markdown document!

Here you can add some text if you want!

 2+3                       
## [1] 5

RMarkDown file!

In this case you obtain a result:

# state to the software what language we are using and if we want to run the code or not 
 2+3                    
## [1] 5
# language is r, if code should lead to the result or not (eval=T or F)

In this case you do not:

 2+3

Using proper packages; the imageRy package has been built at Alma Mater for learning remote sensing:

 library(imageRy)

Lets import some data by listing them in imageRy:

 im.list()                   
##  [1] "dolansprings_oli_2013088_canyon_lrg.jpg"           
##  [2] "EN_01.png"                                         
##  [3] "EN_02.png"                                         
##  [4] "EN_03.png"                                         
##  [5] "EN_04.png"                                         
##  [6] "EN_05.png"                                         
##  [7] "EN_06.png"                                         
##  [8] "EN_07.png"                                         
##  [9] "EN_08.png"                                         
## [10] "EN_09.png"                                         
## [11] "EN_10.png"                                         
## [12] "EN_11.png"                                         
## [13] "EN_12.png"                                         
## [14] "EN_13.png"                                         
## [15] "greenland.2000.tif"                                
## [16] "greenland.2005.tif"                                
## [17] "greenland.2010.tif"                                
## [18] "greenland.2015.tif"                                
## [19] "info.md"                                           
## [20] "iss063e039892_lrg.jpg"                             
## [21] "matogrosso_ast_2006209_lrg.jpg"                    
## [22] "matogrosso_l5_1992219_lrg.jpg"                     
## [23] "NDVI_rainbow.png"                                  
## [24] "NDVI_rainbow_legend.png"                           
## [25] "sentinel.dolomites.b2.tif"                         
## [26] "sentinel.dolomites.b3.tif"                         
## [27] "sentinel.dolomites.b4.tif"                         
## [28] "sentinel.dolomites.b8.tif"                         
## [29] "sentinel.png"                                      
## [30] "Solar_Orbiter_s_first_views_of_the_Sun_pillars.jpg"

Importing the Mato Grosso area image and excluding warnings:

 mato1992 <- im.import("matogrosso_l5_1992219_lrg.jpg")

In order to get information on the image, just type the name of the object:

mato1992                  
## class       : SpatRaster 
## dimensions  : 1500, 1200, 3  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 0, 1200, 0, 1500  (xmin, xmax, ymin, ymax)
## coord. ref. :  
## source      : matogrosso_l5_1992219_lrg.jpg 
## colors RGB  : 1, 2, 3 
## names       : matogrosso~2219_lrg_1, matogrosso~2219_lrg_2, matogrosso~2219_lrg_3

Making a new plot of the Mato Grosso area with the NIR on top of the green component of the RGB space:

im.plotRGB(mato1992, r=2, g=1, b=3)

Plotting several images altogether:

par(mfrow=c(2,2))
im.plotRGB(mato1992, r=1, g=2, b=3) 
im.plotRGB(mato1992, r=2, g=1, b=3)
im.plotRGB(mato1992, r=3, g=2, b=1) 
im.plotRGB(mato1992, r=2, g=1, b=1)

Calculating spectral indices:

library(terra)
## terra 1.7.55
library(viridis)
## Loading required package: viridisLite
dvi <- mato1992[[1]]-mato1992[[2]]
dvi
## class       : SpatRaster 
## dimensions  : 1500, 1200, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 0, 1200, 0, 1500  (xmin, xmax, ymin, ymax)
## coord. ref. :  
## source(s)   : memory
## varname     : matogrosso_l5_1992219_lrg 
## name        : matogrosso_l5_1992219_lrg_1 
## min value   :                        -246 
## max value   :                         255
viridisc <- colorRampPalette(viridis(7))(255)
plot(dvi, col=viridisc)